We compared four orthogonal technologies for sizing, counting, and phenotyping of extracellular vesicles (EVs) and synthetic particles. The platforms were: single‐particle interferometric reflectance imaging sensing (SP‐IRIS) with fluorescence, nanoparticle tracking analysis (NTA) with fluorescence, microfluidic resistive pulse sensing (MRPS), and nanoflow cytometry measurement (NFCM). EVs from the human T lymphocyte line H9 (high CD81, low CD63) and the promonocytic line U937 (low CD81, high CD63) were separated from culture conditioned medium (CCM) by differential ultracentrifugation (dUC) or a combination of ultrafiltration (UF) and size exclusion chromatography (SEC) and characterized by transmission electron microscopy (TEM) and Western blot (WB). Mixtures of synthetic particles (silica and polystyrene spheres) with known sizes and/or concentrations were also tested. MRPS and NFCM returned similar particle counts, while NTA detected counts approximately one order of magnitude lower for EVs, but not for synthetic particles. SP‐IRIS events could not be used to estimate particle concentrations. For sizing, SP‐IRIS, MRPS, and NFCM returned similar size profiles, with smaller sizes predominating (per power law distribution), but with sensitivity typically dropping off below diameters of 60 nm. NTA detected a population of particles with a mode diameter greater than 100 nm. Additionally, SP‐IRIS, MRPS, and NFCM were able to identify at least three of four distinct size populations in a mixture of silica or polystyrene nanoparticles. Finally, for tetraspanin phenotyping, the SP‐IRIS platform in fluorescence mode was able to detect at least two markers on the same particle, while NFCM detected either CD81 or CD63. Based on the results of this study, we can draw conclusions about existing single‐particle analysis capabilities that may be useful for EV biomarker development and mechanistic studies.

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Intense recent interest in understanding how the human gut microbiome influences health has kindled a concomitant interest in linking dietary choices to microbiome variation. Diet is known to be a driver of microbiome variation, and yet the precise mechanisms by which certain dietary components modulate the microbiome, and by which the microbiome produces byproducts and secondary metabolites from dietary components, are not well-understood. Interestingly, despite the influence of diet on the gut microbiome, the majority of microbiome studies published to date contain little or no analysis of dietary intake. Although an increasing number of microbiome studies are now collecting some form of dietary data or even performing diet interventions, there are no clear standards in the microbiome field for how to collect diet data or how to design a diet-microbiome study. In this article, we review the current practices in diet-microbiome analysis and study design and make several recommendations for best practices to provoke broader discussion in the field. We recommend that microbiome studies include multiple consecutive microbiome samples per study timepoint or phase and multiple days of dietary history prior to each microbiome sample whenever feasible. We find evidence that direct effects of diet on the microbiome are likely to be observable within days, while the length of an intervention required for observing microbiome-mediated effects on the host phenotype or host biomarkers, depending on the outcome, may be much longer, on the order of weeks or months. Finally, recent studies demonstrating that dietmicrobiome interactions are personalized suggest that diet-microbiome studies should either include longitudinal sampling within individuals to identify personalized responses, or should include an adequate number of participants spanning a range of microbiome types to identify generalized responses.

Background Human milk oligosaccharides (HMOs) and bioactive breast milk proteins have many beneficial properties. Information is sparse regarding associations between these milk constituents and infant growth and development in lower-income countries. Objectives We aimed to examine associations of milk content of HMOs and bioactive proteins at 6 mo postpartum with infant growth and motor and cognitive development. These are secondary analyses of a randomized controlled trial in rural Malawi. Methods Breast milk samples were analyzed at 6 mo (n = 659) for general categories of HMOs (total HMOs, fucosylated HMOs, and sialylated HMOs), 51 individual HMOs, and 6 bioactive proteins (lactalbumin, lactoferrin, lysozyme, antitrypsin, IgA, and osteopontin). We examined associations of the relative abundances of HMOs and concentrations of bioactive proteins with infant growth from 6 to 12 mo [change in length-for-age (ΔLAZ), weight-for-age, weight-for-length, and head circumference z-scores] as well as ability to stand or walk alone at 12 mo, and motor and language skills, socioemotional development, executive function, and working memory at 18 mo. Analyses were adjusted for covariates and multiple hypothesis testing. Results Among all participants, there were inverse associations of IgA and lactoferrin concentrations with motor skills (P = 0.018 and P = 0.044), and a positive association of lactalbumin concentration with motor skills (P = 0.038). Among secretors only [fucosyltransferase 2 gene (FUT2) positive], there were positive associations of absolute abundance of HMOs with ΔLAZ (P = 0.035), and relative abundance of fucosylated and sialylated HMOs with language at 18 mo (P < 0.001 and P = 0.033, respectively), and inverse associations of osteopontin with standing and walking at 12 mo (P = 0.007 and 0.002, respectively). Relative abundances of several individual HMOs were associated with growth and development, mostly among secretors. Conclusions Certain bioactive breast milk proteins and HMOs are associated with infant growth and motor and cognitive development. Further studies are needed to determine if a causal relation exists. This trial was registered at clinicaltrials.gov as NCT01239693.

As a crucial part of the symbiotic system, the gut microbiome has been shown to be a metabolic organ that presents important connections to many diseases and conditions, including cardiovascular diseases (CVD), the world wide leading cause of death. Trimethylamine N‐oxide (TMAO) is a plasma metabolite that is positively correlated with CVD. Trimethylamine (TMA) is produced by gut bacteria from dietary choline, betaine, or L‐carnitine, and is then converted in the liver to TMAO, which in turn affects hepatic and intestinal lipid and cholesterol metabolism. Because eggs are rich in choline, it has been speculated that their consumption may increase plasma TMAO. The objective of this study was to examine the effect of two eggs per day on plasma TMAO level and how it is related with gut microbiome composition in mildly hypercholesterolemic postmenopausal women. In this randomized, cross‐over study, 20 overweight human subjects were given two whole eggs and the equivalent amount of yolk‐free substitute as breakfast for four weeks, in randomized order, with a four‐week washout in between. Fasting blood draws and stool were collected at the beginning and of each treatment period. Plasma TMAO, choline, betaine and other metabolites were analyzed using LC/MS, while gut microbiome composition was analyzed using 16S amplicon sequencing. Plasma choline and betaine were significantly increased after whole egg but not yolk‐free substitute, however TMAO level was not significantly affected by treatments. Gut microbiome composition showed large inter‐individual variability at baseline and in response to the treatments. Parabacteroides, Ruminococcus, and Faecalibacterium were slightly increased after whole egg but not yolk‐free substitute for the majority of the subjects, while Bilophila slightly increased after yolk‐free substitute but not whole egg. The consumption of two eggs per day in overweight, postmenopausal mildly hypercholesterolemic women significantly increased plasma choline and betaine, but not TMAO, with a subtle effect on gut microbiome composition. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.